English(EN) Workload-Preserving Differentially Private Synthetic Data for Causal Inference via Maximum-Entropy Calibration

新方法生成用于因果推断的差分隐私合成数据

作者 PulseAugur 编辑部 · [2 个来源] · 2026-07-09 05:44

研究人员开发了一种新方法，用于生成专门针对因果推断的差分隐私合成数据。这种方法被称为“因果工作负载”，专注于保留双重稳健因果估计量所需的正交矩，而通用方法则优先考虑整体分布保真度。所提出的技术可以直接使用，或通过最大熵校准进行重建，其理论框架将ATE误差分解为各个组成部分。此外，该研究还引入了用于自适应工作负载选择的Causal-AIM和用于置信区间的NA+MI，使得单个DP合成表能够在没有额外隐私成本的情况下支持多种因果分析。 AI

影响这项研究通过确保合成数据准确反映因果关系，可能在AI系统中实现更强大、更注重隐私的因果推断。

排序理由该集群包含一篇研究论文，详细介绍了一种用于因果推断的差分隐私合成数据生成新方法。

在 arXiv cs.LG 阅读 →

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报道来源 [2]

arXiv cs.LG TIER_1 English(EN) · Amir Asiaee, Kaveh Aryan · 2026-07-10 04:00

Workload-Preserving Differentially Private Synthetic Data for Causal Inference via Maximum-Entropy Calibration

arXiv:2607.08122v1 Announce Type: new Abstract: Workload-based differentially private (DP) synthetic data methods privately measure aggregate queries and post-process the noisy answers into synthetic records. Generic workloads can achieve strong distributional fidelity, but causa…
arXiv cs.LG TIER_1 English(EN) · Kaveh Aryan · 2026-07-09 05:44

Workload-Preserving Differentially Private Synthetic Data for Causal Inference via Maximum-Entropy Calibration

Workload-based differentially private (DP) synthetic data methods privately measure aggregate queries and post-process the noisy answers into synthetic records. Generic workloads can achieve strong distributional fidelity, but causal estimands such as the average treatment effect…

报道来源 [2]

Workload-Preserving Differentially Private Synthetic Data for Causal Inference via Maximum-Entropy Calibration

Workload-Preserving Differentially Private Synthetic Data for Causal Inference via Maximum-Entropy Calibration

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